Vector Space Models for Search and Cluster Mining

  • Mei Kobayashi
  • Masaki Aono


This chapter consists of two parts: a review of search and cluster mining algorithms based on vector space modeling followed by a description of a prototype search and cluster mining system. In the review, we consider Latent Semantic Indexing (LSI), a method based on the Singular Value Decomposition (SVD) of the document attribute matrix and Principal Component Analysis (PCA) of the document vector covariance matrix. In the second part, we present novel techniques for mining major and minor clusters from massive databases based on enhancements of LSI and PCA and automatic labeling of clusters based on their document contents. Most mining systems have been designed to find major clusters and they often fail to report information on smaller minor clusters. Minor cluster identification is important in many business applications, such as detection of credit card fraud, profile analysis, and scientific data analysis. Another novel feature of our method is the recognition and preservation of naturally occurring overlaps among clusters. Cluster overlap analysis is important for multiperspective analysis of databases. Results from implementation studies with a prototype system using over 100,000 news articles demonstrate the effectiveness of search and clustering engines.


Singular Value Decomposition Major Cluster Vector Space Modeling Latent Semantic Indexing Document Vector 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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© Springer Science+Business Media New York 2004

Authors and Affiliations

  • Mei Kobayashi
  • Masaki Aono

There are no affiliations available

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